{"id":"W4200302011","doi":"10.1101/2021.12.03.471134","title":"Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Fondation Brain Canada","keywords":"Hippocampal formation; Computer science; Artificial intelligence; Neuroimaging; Segmentation; Folding (DSP implementation); Hippocampus; Topology (electrical circuits); Neuroscience; Pattern recognition (psychology); Psychology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071094,0.0008393739,0.0007153017,0.002075676,0.0005569269,0.001389209,0.0008716693,0.0009211488,0.003714518],"category_scores_gemma":[0.001635808,0.0005409448,0.0009986107,0.0008321857,0.0006961235,0.0009661636,0.001467223,0.0009555498,0.00146603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006038673,"about_ca_system_score_gemma":0.001122737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003233451,"about_ca_topic_score_gemma":0.006212852,"domain_scores_codex":[0.99972,0.00003828349,0.00001971174,0.00008983687,0.00009639303,0.0000357584],"domain_scores_gemma":[0.9995587,0.0001238937,0.00006853564,0.0001286428,0.00008487529,0.00003535221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000484979,0.0001441332,0.005980607,0.0006688611,0.0004538134,0.0004113291,0.0006294593,0.1770919,0.1024581,0.02458517,0.04271578,0.6443759],"study_design_scores_gemma":[0.00001747356,0.00004055992,0.002065429,0.00003899929,0.00002290275,0.0002350245,0.00009270218,0.941046,0.03117783,0.01672746,0.008497989,0.00003769508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03381951,0.0003251462,0.941874,0.0002503196,0.00006460102,0.00008784891,0.001060862,0.02136461,0.001153089],"genre_scores_gemma":[0.259923,0.0004016378,0.7271867,0.0001721333,0.00006255338,0.0002018911,0.004665586,0.004043978,0.003342494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003714518,"threshold_uncertainty_score":0.01242632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007916817009659996,"score_gpt":0.2360460647976672,"score_spread":0.2281292477880072,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}